Table 5.

Classification accuracy of the predictive models

Model	Accuracy	Training Data	Validation Data
Excluding DS	AUC	0.78 (0.74 to 0.81)	0.73 (0.67 to 0.78)
(Youden=0.23)	Sensitivity	0.65 (0.58 to 0.70)	0.61 (0.52 to 0.69)
	Specificity	0.79 (0.76 to 0.82)	0.74 (0.70 to 0.79)
	PPV	0.48 (0.42 to 0.53)	0.45 (0.37 to 0.53)
	NPV	0.88 (0.85 to 0.90)	0.84 (0.80 to 0.88)
Including DS	AUC	0.87 (0.84 to 0.90)	0.83 (0.79 to 0.88)
(Youden=0.28)	Sensitivity	0.76 (0.70 to 0.81)	0.80 (0.72 to 0.86)
	Specificity	0.85 (0.82 to 0.87)	0.76 (0.72 to 0.81)
	PPV	0.60 (0.55 to 0.66)	0.54 (0.47 to 0.61)
	NPV	0.92 (0.90 to 0.94)	0.91 (0.88 to 0.94)

DS- Disease score, AUC – area under curve, PPV- positive predictive value, NPV- negative predictive value

Measures of discrimination accuracy of the R0 prediction scores. Patients were classified as testing positive or negative using the Youden index estimated from the Training data. Accuracy indicators from the Training and Validation data are shown separately. Patients with predicted R0 probability greater or equal to the Youden index were classified as testing positive, or having a high likelihood of R0 after surgery. PPV is the proportion of patients with a positive test who actually attained R0. NPV is the proportion of patients with a negative test who actually failed to attain R0. Two-sided, α=0.05 Jeffrey’s confidence intervals indicate reliability of the estimates.